This paper proposes the use of Cellular Non-Linear Networks (CNNs) as physical uncloneable functions (PUFs). We argue that analog circuits offer higher security than existing digital PUFs and that the CNN paradigm allows us to build large, unclonable, and scalable analog PUFs, which still show a stable and repeatable input-output behavior. CNNs are dynamical arrays of locally-interconected cells, with a cell dynamics that depends upon the interconnection strengths to their neighbors. They can be designed to evolve in time according to partial differential equations. If this equation describes a physical phenomenon, then the CNN can simulate a complex physical system on-chip. This can be exploited to create electrical PUFs with high relevant structural information content. To illustrate our paradigm at work, we design a circuit that directly emulates nonlinear wave propagation phenomena in a random media. It effectively translates the complexity of optical PUFs into electrical circuits. © 2010 IEEE.
|Original language||English (US)|
|Title of host publication||2010 12th International Workshop on Cellular Nanoscale Networks and their Applications, CNNA 2010|
|Publisher||IEEE Computer Society|
|State||Published - Jan 1 2010|